
import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
    import jax
    import jax.numpy as jnp




@slow
@require_flax
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    return F'''gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase__       ) for s in shape]       )}.npy'''
  def 				_lowercase				(					self     :					int       )     ->      List[Any]:







    """simple docstring"""






    super().tearDown()
    gc.collect()
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Tuple=0     ,				UpperCamelCase__     :					Optional[Any]=(4, 4, 64, 64)     ,				UpperCamelCase__     :					str=False       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			jnp.bfloataa if fpaa else jnp.floataa
    __magic_name__								=			jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__     ,				UpperCamelCase__       )       )     ,				dtype=UpperCamelCase__       )
    return image
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Any=False     ,				UpperCamelCase__     :					Optional[int]="CompVis/stable-diffusion-v1-4"       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			jnp.bfloataa if fpaa else jnp.floataa
    __magic_name__								=			"""bf16""" if fpaa else None

    __magic_name__	,       __magic_name__								=			FlaxUNetaDConditionModel.from_pretrained(
        UpperCamelCase__     ,				subfolder="""unet"""     ,				dtype=UpperCamelCase__     ,				revision=UpperCamelCase__       )
    return model, params
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Any=0     ,				UpperCamelCase__     :					Union[str, Any]=(4, 77, 768)     ,				UpperCamelCase__     :					Tuple=False       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			jnp.bfloataa if fpaa else jnp.floataa
    __magic_name__								=			jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase__     ,				UpperCamelCase__       )       )     ,				dtype=UpperCamelCase__       )
    return hidden_states
  @parameterized.expand(
      [
          # fmt: off
          [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
          [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
          [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
          [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
          # fmt: on
      ]       )
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""     ,				fpaa=UpperCamelCase__       )
    __magic_name__								=			self.get_latents(UpperCamelCase__     ,				fpaa=UpperCamelCase__       )
    __magic_name__								=			self.get_encoder_hidden_states(UpperCamelCase__     ,				fpaa=UpperCamelCase__       )

    __magic_name__								=			model.apply(
        {"""params""": params}     ,				UpperCamelCase__     ,				jnp.array(UpperCamelCase__     ,				dtype=jnp.intaa       )     ,				encoder_hidden_states=UpperCamelCase__     ,				).sample

    assert sample.shape == latents.shape

    __magic_name__								=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())       )     ,				dtype=jnp.floataa       )
    __magic_name__								=			jnp.array(UpperCamelCase__     ,				dtype=jnp.floataa       )

    # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
    assert jnp.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				atol=1E-2       )





  @parameterized.expand(
      [
          # fmt: off
          [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
          [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
          [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
          [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
          # fmt: on
      ]       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""     ,				fpaa=UpperCamelCase__       )
    __magic_name__								=			self.get_latents(UpperCamelCase__     ,				shape=(4, 4, 96, 96)     ,				fpaa=UpperCamelCase__       )
    __magic_name__								=			self.get_encoder_hidden_states(UpperCamelCase__     ,				shape=(4, 77, 1024)     ,				fpaa=UpperCamelCase__       )

    __magic_name__								=			model.apply(
        {"""params""": params}     ,				UpperCamelCase__     ,				jnp.array(UpperCamelCase__     ,				dtype=jnp.intaa       )     ,				encoder_hidden_states=UpperCamelCase__     ,				).sample

    assert sample.shape == latents.shape

    __magic_name__								=			jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())       )     ,				dtype=jnp.floataa       )
    __magic_name__								=			jnp.array(UpperCamelCase__     ,				dtype=jnp.floataa       )

    # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
    assert jnp.allclose(UpperCamelCase__     ,				UpperCamelCase__     ,				atol=1E-2       )



from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			prime_factors(A_   )
  if is_square_free(A_   ):
    return -1 if len(A_   ) % 2 else 1
  return 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()

